Misc. changes

app.py

@@ -13,52 +13,16 @@ import io
 from IPython.display import Image, display, HTML
 from PIL import Image
 import base64 
-
-# removed dotenv and hf key requirements to see how HF Spaces handles it
-
-# In[2]:
+import gradio as gr
 
 
 # Helper function
 import requests, json
 
-#Summarization endpoint
-from transformers import pipeline
-
-get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
-
-def summarize(input):
-    output = get_completion(input)
-    return output[0]['summary_text']
-
-
-
-# ## Building a Named Entity Recognition app
-
-# We are using this [Inference Endpoint](https://huggingface.co/inference-endpoints) for `dslim/bert-base-NER`, a 108M parameter fine-tuned BART model on the NER task.
-
-# ### How about running it locally?
-# 
-# ```py
-# from transformers import pipeline
-# 
-# get_completion = pipeline("ner", model="dslim/bert-base-NER")
-# 
-# def ner(input):
-#     output = get_completion(input)
-#     return {"text": input, "entities": output}
-#     
-# ```
-
-# In[8]:
-
 from transformers import pipeline
 get_completion = pipeline("ner", model="dslim/bert-base-NER")
 
 
-# In[12]:
-
-
 def ner(input):
     output = get_completion(input)
     return {"text": input, "entities": output}
@@ -74,166 +38,3 @@ demo = gr.Interface(fn=ner,
                     examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
 demo.launch()
 
-
-# ### Adding a helper function to merge tokens
-
-# In[17]:
-
-
-def merge_tokens(tokens):
-    merged_tokens = []
-    for token in tokens:
-        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
-            # If current token continues the entity of the last one, merge them
-            last_token = merged_tokens[-1]
-            last_token['word'] += token['word'].replace('##', '')
-            last_token['end'] = token['end']
-            last_token['score'] = (last_token['score'] + token['score']) / 2
-        else:
-            # Otherwise, add the token to the list
-            merged_tokens.append(token)
-
-    return merged_tokens
-
-def ner(input):
-    output = get_completion(input)
-    merged_tokens = merge_tokens(output)
-    return {"text": input, "entities": merged_tokens}
-
-gr.close_all()
-demo = gr.Interface(fn=ner,
-                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
-                    outputs=[gr.HighlightedText(label="Text with entities")],
-                    title="NER with dslim/bert-base-NER",
-                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
-                    allow_flagging="never",
-                    examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])
-
-demo.launch()
-
-
-# In[15]:
-
-
-gr.close_all()
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